Network coding in bi-directed and peer-to-peer networks
Bibliographic record
Abstract
Network coding has been shown to help achieve optimal throughput in directed networks with known link capacities. However, as real-world networks in the Internet are bi-directed in nature, it is important to investigate theoretical and practical advantages of network coding in more realistic bi-directed and peer-to-peer (P2P) network settings. In this chapter, we begin with a discussion of the fundamental limitations of network coding in improving routing throughput and cost in the classic undirected network model. A finite bound of 2 is proved for a single communication session. We then extend the discussions to bi-directed Internet-like networks and to the case of multiple communication sessions. Finally, we investigate advantages of network coding in a practical peer-to-peer network setting, and present both theoretical and experimental results on the use of network coding in P2P content distribution and media streaming. Network coding background Network coding is a fairly recent paradigm of research in information theory and data networking. It allows essentially every node in a network to perform information coding, besides normal forwarding and replication operations. Information flows can therefore be “mixed” during the course of routing. In contrast to source coding, the encoding and decoding operations are not restricted to the terminal nodes (sources and destinations) only, and may happen at all nodes across the network. In contrast to channel coding, network coding works beyond a single communication channel, it contains an integrated coding scheme that dictates the transmission at every link towards a common network-wise goal. The power of network coding can be appreciated with two classic examples in the literature, one for the wireline setting and one for the wireless setting, as shown in Figure 17.1.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".